Abstract

Mining association rules and especially the negativeones has received a lot of attention and has been proved to beuseful in the real world. In this work, a set of algorithms forfinding both positive and negative association rules (NAR) indatabases is presented. A variant of the Apriori, traditionalassociation rules algorithm, is achieved using support andconfidence in order to discover two types of NAR; the confinednegative association rules (CNR), and the generalized negativeassociation rules (GNAR). For the CNR, where only one negativerule exists among positive ones, the negative rule can bediscovered by applying the measure of correlation in terms ofthe conditional and marginal probability along with thecontingency tables. This measure is also used for finding positiverules in the case of branches of itemsets. The negativeassociations of CNR can be used for substitution of items inmarket basket analysis. A method of Binary Tree RulesConstruction (BTRC) has been developed for the discovery ofrules that belong to GNAR , when one or more negative rulesalong with positive ones exist. In each computation process fromdisjoint sets, the BTRC produces nested subtrees in order to findthe NAR. BTRC is based on successive partitioning of the eventsof observing a sequence with a certain number of positive andnegative items. A set of formulas depending on the height of thetree has been developed. The process can be divided into twoparts; the external and the internal subtree process. For thediscovery of both types of rules an algorithm (BTA) is developedbased on a traditional method and the BTRC.

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